Executive Summary

Analytic Data Platforms

Analytic data platforms provide an environment for organizing and managing the storage, processing, analysis, and presentation of data, enabling enterprises to operate with greater efficiency. As enterprises strive to be more data-driven, thus increasing their reliance on data as a fundamental factor in business decision-making, the importance of the analytic data platform has never been greater. Analytic data platforms support applications used to analyze the business, including decision support, business intelligence (BI), data science, and artificial intelligence and machine learning (AI/ML). They include real-time analytics data engines, data warehouses and data lakehouses as well as the increasing convergence of data warehouse, data lake and data-streaming technologies.

Analytic data platforms play a fundamental role in enabling businesses to generate value from their accumulated data.

Analytic data platforms play a fundamental role in enabling businesses to generate value from their accumulated data, supporting BI and data science initiatives that are used to evaluate the business with a view to increasing efficiency, as well as identifying and responding to new business challenges and opportunities. Without analytic data platforms, enterprises would be reliant on a combination of paper records, time-consuming manual processes, and huge libraries of physical files to analyze business information.

Analytic data platforms are complemented by data operations platforms and tools, which are used by data professionals to apply agile development, DevOps and lean manufacturing to data production, as well as data intelligence platforms and tools, which facilitate the understanding of how, when and why data is produced and consumed across an enterprise.

At the heart of any data platform is the storage and management of a collection of related data. This is typically provided by a database management system (more commonly referred to simply as a database) that provides the data persistence, data management, data processing and data query functionality that enables access to, and interaction with, the stored data. Since the 1980s, the market for analytic data platforms has been dominated by the relational data model and relational database management systems.

More recently, data processing frameworks, such as Apache Spark, have been used to form the basis of a data platform by providing data persistence, data management, data processing and data query functionality alongside object storage. Early data lake projects—designed to economically store and process large volumes of raw data—were primarily based on Hadoop. Today, adoption of cloud computing environments has also led to the widespread use of object stores as an underlying data persistence layer for data platforms, with query engines such as Apache Spark, Apache Presto and Trino adding the data processing functionality required of a data platform. We assert that through 2026, three-quarters of enterprises will be using cloud object stores as the primary persistence store for analytic data platform workloads.

While cloud-based object storage provides a low-cost environment for storing large volumes of data, it lacks structured data management and processing functionality to support multiple BI projects as well as data science and operational applications. Accelerating the analysis of data in data lake environments is a key trend driving the analytic data platform space. The data lakehouse concept is designed to incorporate data warehousing functionality—such as structured data management and processing functionality, including support for table formats, metadata management and transactional updates and deletes—into the data lake. Alternatively, a data lake can be used primarily for low-cost storage, while an associated data warehouse provides the ability to persist curated subsets of structured data, apply predetermined schema and enables users to take advantage of established data warehousing functionality for high-performance and high-concurrency query requirements.

Regardless of the approach taken, migration of analytic workloads to the cloud is a significant trend in the analytic data platform sector. Most analytic data platforms were traditionally deployed on-premises, but enterprises are increasingly deploying analytic data platforms on cloud infrastructure or using analytic data platform functionality delivered as managed cloud services. Our research shows that two-thirds of enterprises have their primary data platform for analytics in the cloud. That could be a data lake, a data warehouse, or a combination of the two. One approach does not suit all use cases, and enterprises use a variety of data platforms to fulfill the spectrum of requirements for a myriad of analytic needs.

Another key trend in the data platform sector is the blurring of the lines between operational and analytic workloads. While there have always been general-purpose databases that could be used for both analytic and operational workloads, traditional architectures have involved the extraction, transformation and loading of data from the operational data platform into an external analytic data platform. This enables the operational and analytic workloads to run concurrently without adversely impacting each other, protecting the performance of both.

There is an ongoing need for data platforms designed specifically to support analytic workloads.

The development of intelligent applications infused with contextually relevant recommendations, predictions and forecasting driven by ML and generative AI (GenAI) provides a set of workloads that span traditional requirements. While this impacts the requirements for operational data platforms to support real-time analytic functionality, it does not eradicate the need for analysis of data in a separate analytic data platform to support BI and data science projects, as well as the development, training and fine-tuning of AI models. There is an ongoing need for data platforms designed specifically to support analytic workloads, with dedicated functionality for data engineering, including the development, training and tuning of ML and GenAI models. The Analytic Data Platforms Buyers Guide reflects this by assessing products positioned as analytic data platforms on their ability to serve the specific requirements of analytic use cases.

Separately, we have also created the Operational Data Platforms Buyers Guide, which excludes dedicated analytic functionality and data platforms. Meanwhile, the Data Platforms Buyers Guide evaluates a software provider’s ability to serve a combination of both operational and analytic workloads, taking into account the analytic processing capabilities of operational data platforms, and vice versa. Our assessments also considered whether the functionality in question was available from a software provider in a single offering or as a suite of products or cloud services.

Ventana Research believes a methodical approach is essential to maximize competitiveness. To improve the performance of an enterprise’s people, process, information and technology components, it is critical to select the right software provider and product. Many enterprises need to improve in this regard. Our research analysis places fewer than 1 in 5 enterprises (18%) at the highest Innovative level of performance in their use of analytics and data. However, caution is appropriate here—technology improvements alone are not enough to improve the use of data in an enterprise. Doing so requires applying a balanced set of upgrades that include efforts to improve both worker skills and processes. The research finds that fewer than 1 in 6 enterprises (15%) are at the highest Innovative level of performance for process in relation to analytics and data, and fewer than 1 in 8 (12%) are at the Innovative level of performance for people.

To be considered for inclusion in the Analytic Data Platforms Buyers Guide, a product must be marketed as a general-purpose data platform, database, database management system, data warehouse, data lake, or data lakehouse. The primary use case for the product should be to support analytics workloads (such as BI or data science). The product should provide the following functional areas at a minimum: data persistence, data management, data processing and data query; database administrator functionality; developer functionality; data engineering functionality; and data architect functionality.

This Buyers Guide report evaluates the following software providers which offer products that are considered analytic data platforms as we define it: Actian, Aiven, Alibaba Cloud, AWS, Cloudera, Couchbase, Databricks, Dremio, EDB, Exasol, Google Cloud, Huawei Cloud, IBM, Incorta, InterSystems, KX, MariaDB, Microsoft, MongoDB, Neo4j, Oracle, OpenText, Percona, PingCAP, Progress Software, Salesforce, SAP, SingleStore, Snowflake, SQream, Starburst, Tencent Cloud, Teradata, TigerGraph and VMware by Broadcom.

Buyers Guide Overview

For over two decades, Ventana Research has conducted market research in a spectrum of areas across business applications, tools and technologies. We have designed the Buyers Guide to provide a balanced perspective of software providers and products that is rooted in an understanding of the business requirements in any enterprise. Utilization of our research methodology and decades of experience enables our Buyers Guide to be an effective method to assess and select software providers and products. The findings of this research undertaking contribute to our comprehensive approach to rating software providers in a manner that is based on the assessments completed by an enterprise.

Ventana Research has designed the Buyers Guide to provide a balanced perspective of software providers and products that is rooted in an understanding of business requirements in any enterprise.

This Ventana Research Buyers Guide: Analytic Data Platforms is the distillation of over a year of market and product research efforts. It is an assessment of how well software providers’ offerings address enterprises’ requirements for analytic data platform software. The index is structured to support a request for information (RFI) that could be used in the request for proposal (RFP) process by incorporating all criteria needed to evaluate, select, utilize and maintain relationships with software providers. An effective product and customer experience with a provider can ensure the best long-term relationship and value achieved from a resource and financial investment.

In this Buyers Guide, Ventana Research evaluates the software in seven key categories that are weighted to reflect buyers’ needs based on our expertise and research. Five are product-experience related: Adaptability, Capability, Manageability, Reliability, and Usability. In addition, we consider two customer-experience categories: Validation, and Total Cost of Ownership/Return on Investment (TCO/ROI). To assess functionality, one of the components of Capability, we applied the Ventana Research Value Index methodology and blueprint, which links the personas and processes for analytic data platforms to an enterprise’s requirements.

The structure of the research reflects our understanding that the effective evaluation of software providers and products involves far more than just examining product features, potential revenue or customers generated from a provider’s marketing and sales efforts. We believe it is important to take a comprehensive, research-based approach, since making the wrong choice of analytic data platform technology can raise the total cost of ownership, lower the return on investment and hamper an enterprise’s ability to reach its full performance potential. In addition, this approach can reduce the project’s development and deployment time and eliminate the risk of relying on a short list of software providers that does not represent a best fit for your enterprise.

Ventana Research believes that an objective review of software providers and products is a critical business strategy for the adoption and implementation of analytic data platform software and applications. An enterprise’s review should include a thorough analysis of both what is possible and what is relevant. We urge enterprises to do a thorough job of evaluating analytic data platforms systems and tools and offer this Buyers Guide as both the results of our in-depth analysis of these providers and as an evaluation methodology.

How To Use This Buyers Guide

Evaluating Software Providers: The Process

We recommend using the Buyers Guide to assess and evaluate new or existing software providers for your enterprise. The market research can be used as an evaluation framework to establish a formal request for information from providers on products and customer experience and will shorten the cycle time when creating an RFI. The steps listed below provide a process that can facilitate best possible outcomes.

1. Define the business case and goals.
   Define the mission and business case for investment and the expected outcomes from your organizational and technology efforts. 
2. Specify the business needs.
   Defining the business requirements helps identify what specific capabilities are required with respect to people, processes, information and technology.
3. Assess the required roles and responsibilities.
   Identify the individuals required for success at every level of the organization from executives to front line workers and determine the needs of each. 
4. Outline the project’s critical path.
   What needs to be done, in what order and who will do it? This outline should make clear the prior dependencies at each step of the project plan. 
5. Ascertain the technology approach.
   Determine the business and technology approach that most closely aligns to your organization’s requirements. 
6. Establish technology vendor evaluation criteria.
   Utilize the product experience: Adaptability, Capability, Manageability, Reliability and Usability, and the customer experience in TCO/ROI and Validation. 
7. Evaluate and select the technology properly.
   Weight the categories in the technology evaluation criteria to reflect your organization’s priorities to determine the short list of vendors and products.
8. Establish the business initiative team to start the project.
   Identify who will lead the project and the members of the team needed to plan and execute it with timelines, priorities and resources.

The Findings

All of the products we evaluated are feature-rich, but not all the capabilities offered by a software provider are equally valuable to types of workers or support everything needed to manage products on a continuous basis. Moreover, the existence of too many capabilities may be a negative factor for an enterprise if it introduces unnecessary complexity. Nonetheless, you may decide that a larger number of features in the product is a plus, especially if some of them match your enterprise’s established practices or support an initiative that is driving the purchase of new software.

Factors beyond features and functions or software provider assessments may become a deciding factor. For example, an enterprise may face budget constraints such that the TCO evaluation can tip the balance to one provider or another. This is where the Value Index methodology and the appropriate category weighting can be applied to determine the best fit of software providers and products to your specific needs.

Overall Scoring of Software Providers Across Categories

The research finds Oracle atop the list, followed by Teradata and IBM. Companies that place in the top three of a category earn the designation of Leader. Oracle has done so in five categories; SAP in four; Databricks in three; and Google Cloud and InterSystems in two. Actian, AWS, IBM, Microsoft and Teradata were all designated a Leader in one category each.

The overall representation of the research below places the rating of the Product Experience and Customer Experience on the x and y axes, respectively, to provide a visual representation and classification of the software providers. Those providers whose Product Experience have a higher weighted performance to the axis in aggregate of the five product categories place farther to the right, while the performance and weighting for the two Customer Experience categories determines placement on the vertical axis. In short, software providers that place closer to the upper-right on this chart performed better than those closer to the lower-left.

The research places software providers into one of four overall categories: Assurance, Exemplary, Merit or Innovative. This representation classifies providers’ overall weighted performance.

Exemplary: The categorization and placement of software providers in Exemplary (upper right) represent those that performed the best in meeting the overall Product and Customer Experience requirements. The providers rated Exemplary are: Actian, AWS, Couchbase, Databricks, Google Cloud, IBM, InterSystems, Microsoft, MongoDB, Oracle, Salesforce, SAP, Snowflake and Teradata.

Innovative: The categorization and placement of software providers in Innovative (lower right) represent those that performed the best in meeting the overall Product Experience requirements but did not achieve the highest levels of requirements in Customer Experience. The providers rated Innovative are: Alibaba Cloud, Cloudera, OpenText and VMware by Broadcom.

Assurance: The categorization and placement of software providers in Assurance (upper left) represent those that achieved the highest levels in the overall Customer Experience requirements but did not achieve the highest levels of Product Experience. The providers rated Assurance are: EDB, Incorta, Neo4j and SingleStore.

Merit: The categorization of software providers in Merit (lower left) represents those that did not exceed the median of performance in Customer or Product Experience or surpass the threshold for the other three categories. The providers rated Merit are: Aiven, Dremio, EXASOL, Huawei Cloud, KX, MariaDB, Percona, PingCAP, Progress Software, SQream, Starburst Data, Tencent Cloud and TigerGraph.

We warn that close provider placement proximity should not be taken to imply that the packages evaluated are functionally identical or equally well suited for use by every enterprise or for a specific process. Although there is a high degree of commonality in how enterprises handle analytic data platforms, there are many idiosyncrasies and differences in how they do these functions that can make one software provider’s offering a better fit than another’s for a particular enterprise’s needs.

We advise enterprises to assess and evaluate software providers based on organizational requirements and use this research as a supplement to internal evaluation of a provider and products.

Product Experience

The process of researching products to address an enterprise’s needs should be comprehensive. Our Value Index methodology examines Product Experience and how it aligns with an enterprise’s life cycle of onboarding, configuration, operations, usage and maintenance. Too often, software providers are not evaluated for the entirety of the product; instead, they are evaluated on market execution and vision of the future, which are flawed since they do not represent an enterprise’s requirements but how the provider operates. As more software providers orient to a complete product experience, evaluations will be more robust.

The research based on the methodology of expertise identified the weighting of Product Experience to 80% or four-fifths of the overall rating. Importance was placed on the categories as follows: Usability (10%), Capability (25%), Reliability (15%), Adaptability (15%) and Manageability (15%). This weighting impacted the resulting overall ratings in this research. Oracle, Teradata and Google Cloud were designated Product Experience Leaders. While not a Leader, Microsoft, AWS and SAP were also found to meet a broad range of enterprise analytic data platforms requirements.

Many enterprises will only evaluate capabilities for workers in IT or administration, but the research identified the criticality of Usability (10% weighting) across a broader set of usage personas that should participate in analytic data platforms.

Customer Experience

The importance of a customer relationship with a software provider is essential to the actual success of the products and technology. The advancement of the Customer Experience and the entire life cycle an enterprise has with its software provider is critical for ensuring satisfaction in working with that provider. Technology providers that have chief customer officers are more likely to have greater investments in the customer relationship and focus more on their success. These leaders also need to take responsibility for ensuring this commitment is made abundantly clear on the website and in the buying process and customer journey.

Our Value Index methodology weights Customer Experience at 20% of the overall rating, or one-fifth, as it relates to the framework of commitment and value to the software provider-customer relationship. The two evaluation categories are Validation (10%) and TCO/ROI (10%), which are weighted to represent their importance to the overall research.

The software providers that evaluated the highest overall in the aggregated and weighted Customer Experience categories are Databricks, Microsoft and SAP. These category leaders best communicate commitment and dedication to customer needs.

Many software providers we evaluated did not have sufficient information available through their website and presentations. While many have customer case studies to promote success, others lack depth in articulating their commitment to customer experience and an enterprise’s analytic data platforms journey. As the commitment to a software provider is a continuous investment, the importance of supporting customer experience in a holistic evaluation should be included and not underestimated.

Appendix: Software Provider Inclusion

For inclusion in the Ventana Research 2024 Analytic Data Platforms Buyers Guide, a provider must be in good standing financially and ethically, sell products and provide support on at least two continents, and have at least $100 million in annual or projected revenue, or at least 50 customers. The principal source of the relevant business unit’s revenue has to be software-related and there must have been at least one major software release in the last 12 months. The product must be marketed as a data platform, database, database management system, data warehouse, data lake or data lakehouse, and the primary use case for the product should be to support worker- and customer-facing operational applications (such as financial, resource planning, human resources, customer management/experience, ecommerce or supply chain) and/or analytics workloads (business intelligence or data science). The provider must have a product that provides the following functional areas at a minimum, which are mapped into Buyers Guide capability criteria:     

• Core database functionality (data persistence, management, processing and query) 
• Database administrator functionality   
• Developer functionality   
• Data engineer functionality  
• Data architect functionality 

The research is designed to be independent of the specifics of software provider packaging and pricing. To represent the real-world environment in which businesses operate, we include providers that offer suites or packages of products that may include relevant individual modules or applications. If a software provider is actively marketing, selling and developing a product for the general market and it is reflected on the provider’s website that the product is within the scope of the research, that provider is automatically evaluated for inclusion.

All software providers that offer relevant analytic data platform products and meet the inclusion requirements were invited to participate in the evaluation process at no cost to them.

Software providers that meet our inclusion criteria but did not completely participate in our Buyers Guide were assessed solely on publicly available information. As this could have a significant impact on classification and ratings, we recommend additional scrutiny when evaluating those providers.

Products Evaluated

Providers of Promise

We did not include software providers that, as a result of our research and analysis, did not satisfy the criteria for inclusion in this Buyers Guide. These are listed below as “Providers of Promise.”